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Timeseries queries
These types of queries take a timeseries query object and return an array of JSON objects where each object represents a value asked for by the timeseries query.
An example timeseries query object is shown below:
{
"queryType": "timeseries",
"dataSource": "sample_datasource",
"granularity": "day",
"filter": {
"type": "and",
"fields": [
{ "type": "selector", "dimension": "sample_dimension1", "value": "sample_value1" },
{ "type": "or",
"fields": [
{ "type": "selector", "dimension": "sample_dimension2", "value": "sample_value2" },
{ "type": "selector", "dimension": "sample_dimension3", "value": "sample_value3" }
]
}
]
},
"aggregations": [
{ "type": "longSum", "name": "sample_name1", "fieldName": "sample_fieldName1" },
{ "type": "doubleSum", "name": "sample_name2", "fieldName": "sample_fieldName2" }
],
"postAggregations": [
{ "type": "arithmetic",
"name": "sample_divide",
"fn": "/",
"fields": [
{ "type": "fieldAccess", "name": "sample_name1", "fieldName": "sample_fieldName1" },
{ "type": "fieldAccess", "name": "sample_name2", "fieldName": "sample_fieldName2" }
]
}
],
"intervals": [ "2012-01-01T00:00:00.000/2012-01-03T00:00:00.000" ]
}
There are 7 main parts to a timeseries query:
property | description | required? |
---|---|---|
queryType | This String should always be "timeseries"; this is the first thing Druid looks at to figure out how to interpret the query | yes |
dataSource | A String defining the data source to query, very similar to a table in a relational database | yes |
granularity | Defines the granularity of the query. See Granularities | yes |
filter | See Filters | no |
aggregations | See Aggregations | yes |
postAggregations | See Post Aggregations | no |
intervals | A JSON Object representing ISO-8601 Intervals. This defines the time ranges to run the query over. | yes |
context | An additional JSON Object which can be used to specify certain flags. | no |
To pull it all together, the above query would return 2 data points, one for each day between 2012-01-01 and 2012-01-03, from the "sample_datasource" table. Each data point would be the (long) sum of sample_fieldName1, the (double) sum of sample_fieldName2 and the (double) the result of sample_fieldName1 divided by sample_fieldName2 for the filter set. The output looks like this:
[
{
"timestamp": "2012-01-01T00:00:00.000Z",
"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
},
{
"timestamp": "2012-01-02T00:00:00.000Z",
"result": { "sample_name1": <some_value>, "sample_name2": <some_value>, "sample_divide": <some_value> }
}
]